Leaf dry matter content is better at predicting above‐ground net primary production than specific leaf area. (27th February 2017)

• Record Type: Journal Article
• Authors: Smart, Simon Mark; Glanville, Helen Catherine; Blanes, Maria del Carmen; Mercado, Lina Maria; Emmett, Bridget Anne; Jones, David Leonard; Cosby, Bernard Jackson; Marrs, Robert Hunter; Butler, Adam; Marshall, Miles Ramsvik; Reinsch, Sabine; Herrero‐Jáuregui, Cristina; Hodgson, John Gavin
• Editors: Field, Katie
• Is Part Of: Functional ecology. Volume 31:Number 6(2017)
• Page Start: 1336
• Page End: 1344
• Publication Date: 2017-02-27
• Subjects: Bayesian modelling -- ecosystem function -- global change -- intraspecific variation -- measurement error; Ecology -- Periodicals; 574.505
• DOI: 10.1111/1365-2435.12832
• Languages: English
• ISSNs: 0269-8463

Abstract

Summary: Reliable modelling of above‐ground net primary production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. We compared abundance‐weighted values of two of the most widely used traits from the leaf economics spectrum (specific leaf area and leaf dry matter content) with measured aNPP across a temperate ecosystem gradient. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP ( R 2 = 0·55). Directly measured in situ trait values for the dominant species improved estimation of aNPP significantly. Introducing intraspecific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA. A lay summary is available for this article. Abstract : Lay Summary